Strategic Integration and Devolvement of Human Resource Management in the UK Manufacturing Sector

This paper analyses the practices of ‘integration’ of HRM into the corporate strategy and ‘devolvement’ of responsibility for HRM to line managers in six British manufacturing industries. The findings are based on a questionnaire survey, in‐depth interviews and cognitive mapping methodologies. The results show that over 50% of the firms under study practise a high level of strategic integration. On the other hand, over 61% of the sample firms practise a low level of devolvement practices. Interestingly, both the practices of integration of HRM into the corporate strategy and devolvement of HRM to line managers are more determined by a number of organizational policies than traditional contingent variables. The adoption of the mixed methodology has been useful. The findings contribute to strategic HRM literature, and also have some key messages for policy‐makers in the field. The cognitive maps developed in the paper could be used to give feedback and training to managers.     

Flood Hazard and Flood Risk Assessment Using a Time Series of Satellite Images: A Case Study in Namibia

In this article, the use of time series of satellite imagery to flood hazard mapping and flood risk assessment is presented. Flooded areas are extracted from satellite images for the flood‐prone territory, and a maximum flood extent image for each flood event is produced. These maps are further fused to determine relative frequency of inundation (RFI). The study shows that RFI values and relative water depth exhibit the same probabilistic distribution, which is confirmed by Kolmogorov‐Smirnov test. The produced RFI map can be used as a flood hazard map, especially in cases when flood modeling is complicated by lack of available data and high uncertainties. The derived RFI map is further used for flood risk assessment. Efficiency of the presented approach is demonstrated for the Katima Mulilo region (Namibia). A time series of Landsat‐5/7 satellite images acquired from 1989 to 2012 is processed to derive RFI map using the presented approach. The following direct damage categories are considered in the study for flood risk assessment: dwelling units, roads, health facilities, and schools. The produced flood risk map shows that the risk is distributed uniformly all over the region. The cities and villages with the highest risk are identified. The proposed approach has minimum data requirements, and RFI maps can be generated rapidly to assist rescuers and decisionmakers in case of emergencies. On the other hand, limitations include: strong dependence on the available data sets, and limitations in simulations with extrapolated water depth values.     

Cognitive Mapping Tools: Review and Risk Management Needs

Risk managers are increasingly interested in incorporating stakeholder beliefs and other human factors into the planning process. Effective risk assessment and management requires understanding perceptions and beliefs of involved stakeholders, and how these beliefs give rise to actions that influence risk management decisions. Formal analyses of risk manager and stakeholder cognitions represent an important first step. Techniques for diagramming stakeholder mental models provide one tool for risk managers to better understand stakeholder beliefs and perceptions concerning risk, and to leverage this new understanding in developing risk management strategies. This article reviews three methodologies for assessing and diagramming stakeholder mental models—decision‐analysis‐based mental modeling, concept mapping, and semantic web analysis—and assesses them with regard to their ability to address risk manager needs.     

RAILROADS AND GROWTH IN PRUSSIA

We study the effect of railroad access on urban population growth. Using GIS techniques, we match triennial population data for roughly 1,000 cities in 19th‐century Prussia to georeferenced maps of the German railroad network. We find positive short‐ and long‐term effects of having a station on urban growth for different periods during 1840–1871. Causal effects of (potentially endogenous) railroad access on city growth are identified using propensity score matching, instrumental variables, and fixed‐effects estimation techniques. Our instrument identifies exogenous variation in railroad access by constructing straight‐line corridors between nodes. Counterfactual models using pre‐railroad growth yield no evidence to support the hypothesis that railroads appeared as a consequence of a previous growth spurt.     

Improving Causal Inferences in Risk Analysis

Recent headlines and scientific articles projecting significant human health benefits from changes in exposures too often depend on unvalidated subjective expert judgments and modeling assumptions, especially about the causal interpretation of statistical associations. Some of these assessments are demonstrably biased toward false positives and inflated effects estimates. More objective, data‐driven methods of causal analysis are available to risk analysts. These can help to reduce bias and increase the credibility and realism of health effects risk assessments and causal claims. For example, quasi‐experimental designs and analysis allow alternative (noncausal) explanations for associations to be tested, and refuted if appropriate. Panel data studies examine empirical relations between changes in hypothesized causes and effects. Intervention and change‐point analyses identify effects (e.g., significant changes in health effects time series) and estimate their sizes. Granger causality tests, conditional independence tests, and counterfactual causality models test whether a hypothesized cause helps to predict its presumed effects, and quantify exposure‐specific contributions to response rates in differently exposed groups, even in the presence of confounders. Causal graph models let causal mechanistic hypotheses be tested and refined using biomarker data. These methods can potentially revolutionize the study of exposure‐induced health effects, helping to overcome pervasive false‐positive biases and move the health risk assessment scientific community toward more accurate assessments of the impacts of exposures and interventions on public health.     

Linking Managerial Cognition and Organizational Performance: A Preliminary Investigation Using Causal Maps

This paper considers the potential relationship between individual cognition and organizational performance. A series of causal maps are elicited from the owner-managers of retail businesses which are either growing or static and contracting. The maps are compared using a series of propositions to establish whether individual cognition is consistent with the contrasts which would be expected between relatively high and low performing businesses. When the general characteristics of the maps are compared no significant differences are found when the propositions are evaluated. However, a subsequent inductive phase of analysis suggests that more detailed insights can be gained through a focus on the relationships between specific types of concept within the individual maps. In contrast to the assumptions made in many mapping studies, this finding suggests that it is the idiosyncratic details of map content and structure which provide the basis for exploring the relationship between cognition and performance, rather than the overall characteristic of the maps.     

Combining Experts’ Judgments: Comparison of Algorithmic Methods Using Synthetic Data

Expert judgment (or expert elicitation) is a formal process for eliciting judgments from subject‐matter experts about the value of a decision‐relevant quantity. Judgments in the form of subjective probability distributions are obtained from several experts, raising the question how best to combine information from multiple experts. A number of algorithmic approaches have been proposed, of which the most commonly employed is the equal‐weight combination (the average of the experts’ distributions). We evaluate the properties of five combination methods (equal‐weight, best‐expert, performance, frequentist, and copula) using simulated expert‐judgment data for which we know the process generating the experts’ distributions. We examine cases in which two well‐calibrated experts are of equal or unequal quality and their judgments are independent, positively or negatively dependent. In this setting, the copula, frequentist, and best‐expert approaches perform better and the equal‐weight combination method performs worse than the alternative approaches.     

A dynamic ERP critical failure factors modelling with FCM throughout project lifecycle phases

Implementation of enterprise resource planning systems (ERPs) is a complex and costly task which usually results in serious failures. Numerous factors affect these projects implementation due to their size, complexity and high chance of failure. Therefore, identifying these factors in ERP projects is a critical issue. The majority of previous publications have been conducted in identifying ERP critical success factors (CSFs) rather than critical failure factors (CFFs). In order to help practitioners, this article studies the CFFs in ERP implementation projects. The implications of interdependency among failure factors are also usually overlooked by project managers due to perceived complexity in modelling and analysing influential factors. With this in mind, we have proposed a fuzzy cognitive map (FCM)-based dynamic model of ERP failure factors through project lifecycle phases. The main advantage of FCM lies in them being capable of modelling complex phenomena based on the experts’ perceptions. This tool models uncertainty and related events, imitating human reasoning. Moreover, FCMs enable the developing of forecasting exercises through simulations. Practitioners would thus assess the joint influence of ERP implementation failure factors on project outcomes. The results make known to practitioners which problems will arise if the failure factors are not treated, and how these will impact on the outcomes of projects. Therefore, the proposed approach would help them to manage ERP implementation projects in a more effective and proactive way.     

Evidence maps: communicating risk assessments in societal controversies: the case of engineered nanoparticles

The transparent and fair characterization of scientific evidence for reporting the results of a hazard assessment is a demanding task. In this article, we present an approach for characterizing evidence--the evidence map approach. The theoretical starting point is to view evidence characterization as a form of argumentation. Thus, evidence maps are designed to depict the evidence base, the pro and con arguments, and the remaining uncertainties, which together lead experts to their conclusions when summarizing and evaluating the scientific evidence about a potential hazard. To illustrate its use, the evidence maps approach is applied to characterizing the health-relevant effects of engineered nanoparticles. Empirical data from an online survey suggests that the use of evidence maps improves the reporting of hazard assessments. Nonexperts prefer to receive the information included in an evidence map in order to come to an informed judgment. Furthermore, the benefits and limitations of evidence maps are discussed in the light of recent literature on risk communication. Finally, the article underlines the need for further research in order to increase quality of evidence reporting.     

Opportunism in Capital Budget Recommendations: The Effects of Past Performance and Its Attributions*

This study uses an experiment to examine the separate and combined effects of managers' loss aversion and their causal attributions about their divisions' performance on tendencies to make goal‐incongruent capital budget recommendations. We find that managers' recommendations are biased by their loss aversion. In particular, managers of high‐performing divisions are more likely than managers of low‐performing divisions to propose investments that maximize their division's short‐term profits at the expense of the firm's long‐term value. We also find that managers' recommendations are biased by their causal attributions. In particular, managers are more likely to propose investments that maximize their division's short‐term profits at the expense of the firm's long‐term value when they attribute their division's performance to external causes (e.g., task difficulty or luck) rather than to internal causes (e.g., managerial ability or effort). Further, the effects of causal attributions are greater for managers of high‐performing divisions than for managers of low‐performing divisions. The study's findings are important because loss aversion and causal attributions are often manifested in firms. Thus, they may bias managers' decisions, which in turn may be detrimental to the firms' long‐term value.     

Assessment and Application of National Environmental Databases and Mapping Tools at the Local Level to Two Community Case Studies

Communities are concerned over pollution levels and seek methods to systematically identify and prioritize the environmental stressors in their communities. Geographic information system (GIS) maps of environmental information can be useful tools for communities in their assessment of environmental‐pollution‐related risks. Databases and mapping tools that supply community‐level estimates of ambient concentrations of hazardous pollutants, risk, and potential health impacts can provide relevant information for communities to understand, identify, and prioritize potential exposures and risk from multiple sources. An assessment of existing databases and mapping tools was conducted as part of this study to explore the utility of publicly available databases, and three of these databases were selected for use in a community‐level GIS mapping application. Queried data from the U.S. EPA's National‐Scale Air Toxics Assessment, Air Quality System, and National Emissions Inventory were mapped at the appropriate spatial and temporal resolutions for identifying risks of exposure to air pollutants in two communities. The maps combine monitored and model‐simulated pollutant and health risk estimates, along with local survey results, to assist communities with the identification of potential exposure sources and pollution hot spots. Findings from this case study analysis will provide information to advance the development of new tools to assist communities with environmental risk assessments and hazard prioritization.     

The Influence of Uncertain Map Features on Risk Beliefs and Perceived Ambiguity for Maps of Modeled Cancer Risk from Air Pollution

Maps are often used to convey information generated by models, for example, modeled cancer risk from air pollution. The concrete nature of images, such as maps, may convey more certainty than warranted for modeled information. Three map features were selected to communicate the uncertainty of modeled cancer risk: (i) map contours appeared in or out of focus, (ii) one or three colors were used, and (iii) a verbal‐relative or numeric risk expression was used in the legend. Study aims were to assess how these features influenced risk beliefs and the ambiguity of risk beliefs at four assigned map locations that varied by risk level. We applied an integrated conceptual framework to conduct this full factorial experiment with 32 maps that varied by the three dichotomous features and four risk levels; 826 university students participated. Data was analyzed using structural equation modeling. Unfocused contours and the verbal‐relative risk expression generated more ambiguity than their counterparts. Focused contours generated stronger risk beliefs for higher risk levels and weaker beliefs for lower risk levels. Number of colors had minimal influence. The magnitude of risk level, conveyed using incrementally darker shading, had a substantial dose‐response influence on the strength of risk beliefs. Personal characteristics of prior beliefs and numeracy also had substantial influences. Bottom‐up and top‐down information processing suggest why iconic visual features of incremental shading and contour focus had the strongest visual influences on risk beliefs and ambiguity. Variations in contour focus and risk expression show promise for fostering appropriate levels of ambiguity.     

A New Multicriteria Risk Mapping Approach Based on a Multiattribute Frontier Concept

Invasive species risk maps provide broad guidance on where to allocate resources for pest monitoring and regulation, but they often present individual risk components (such as climatic suitability, host abundance, or introduction potential) as independent entities. These independent risk components are integrated using various multicriteria analysis techniques that typically require prior knowledge of the risk components’ importance. Such information is often nonexistent for many invasive pests. This study proposes a new approach for building integrated risk maps using the principle of a multiattribute efficient frontier and analyzing the partial order of elements of a risk map as distributed in multidimensional criteria space. The integrated risks are estimated as subsequent multiattribute frontiers in dimensions of individual risk criteria. We demonstrate the approach with the example of Agrilus biguttatus Fabricius, a high‐risk pest that may threaten North American oak forests in the near future. Drawing on U.S. and Canadian data, we compare the performance of the multiattribute ranking against a multicriteria linear weighted averaging technique in the presence of uncertainties, using the concept of robustness from info‐gap decision theory. The results show major geographic hotspots where the consideration of tradeoffs between multiple risk components changes integrated risk rankings. Both methods delineate similar geographical regions of high and low risks. Overall, aggregation based on a delineation of multiattribute efficient frontiers can be a useful tool to prioritize risks for anticipated invasive pests, which usually have an extremely poor prior knowledge base.     

Understanding Community Resilience from a PRA Perspective Using Binary Decision Diagrams

Probabilistic risk assessment (PRA) is a useful tool to assess complex interconnected systems. This article leverages the capabilities of PRA tools developed for industrial and nuclear risk analysis in community resilience evaluations by modeling the food security of a community in terms of its built environment as an integrated system. To this end, we model the performance of Gilroy, CA, a moderate‐size town, with regard to disruptions in its food supply caused by a severe earthquake. The food retailers of Gilroy, along with the electrical power network, water network elements, and bridges are considered as components of a system. Fault and event trees are constructed to model the requirements for continuous food supply to community residents and are analyzed efficiently using binary decision diagrams (BDDs). The study also identifies shortcomings in approximate classical system analysis methods in assessing community resilience. Importance factors are utilized to rank the importance of various factors to the overall risk of food insecurity. Finally, the study considers the impact of various sources of uncertainties in the hazard modeling and performance of infrastructure on food security measures. The methodology can be applicable for any existing critical infrastructure system and has potential extensions to other hazards.     

Is Risk Analysis Scientific?

This article discusses to what extent risk analysis is scientific in view of a set of commonly used definitions and criteria. We consider scientific knowledge to be characterized by its subject matter, its success in developing the best available knowledge in its fields of study, and the epistemic norms and values that guide scientific investigations. We proceed to assess the field of risk analysis according to these criteria. For this purpose, we use a model for risk analysis in which science is used as a base for decision making on risks, which covers the five elements evidence, knowledge base, broad risk evaluation, managerial review and judgment, and the decision; and that relates these elements to the domains experts and decisionmakers, and to the domains fact‐based or value‐based. We conclude that risk analysis is a scientific field of study, when understood as consisting primarily of (i) knowledge about risk‐related phenomena, processes, events, etc., and (ii) concepts, theories, frameworks, approaches, principles, methods and models to understand, assess, characterize, communicate, and manage risk, in general and for specific applications (the instrumental part).     

Estimating Conditional Probabilities of Terrorist Attacks: Modeling Adversaries with Uncertain Value Tradeoffs

Many analyses conducted to inform security decisions depend on estimates of the conditional probabilities of different attack alternatives. These probabilities are difficult to estimate since analysts have limited access to the adversary and limited knowledge of the adversary’s utility function, so subject matter experts often provide the estimates through direct elicitation. In this article, we describe a method of using uncertainty in utility function value tradeoffs to model the adversary’s decision process and solve for the conditional probabilities of different attacks in closed form. The conditional probabilities are suitable to be used as inputs to probabilistic risk assessments and other decision support techniques. The process we describe is an extension of value‐focused thinking and is broadly applicable, including in general business decision making. We demonstrate the use of this technique with simple examples.     

An Assessment of Change in Risk Perception and Optimistic Bias for Hurricanes Among Gulf Coast Residents

This study focuses on levels of concern for hurricanes among individuals living along the Gulf Coast during the quiescent two‐year period following the exceptionally destructive 2005 hurricane season. A small study of risk perception and optimistic bias was conducted immediately following Hurricanes Katrina and Rita. Two years later, a follow‐up was done in which respondents were recontacted. This provided an opportunity to examine changes, and potential causal ordering, in risk perception and optimistic bias. The analysis uses 201 panel respondents who were matched across the two mail surveys. Measures included hurricane risk perception, optimistic bias for hurricane evacuation, past hurricane experience, and a small set of demographic variables (age, sex, income, and education). Paired t‐tests were used to compare scores across time. Hurricane risk perception declined and optimistic bias increased. Cross‐lagged correlations were used to test the potential causal ordering between risk perception and optimistic bias, with a weak effect suggesting the former affects the latter. Additional cross‐lagged analysis using structural equation modeling was used to look more closely at the components of optimistic bias (risk to self vs. risk to others). A significant and stronger potentially causal effect from risk perception to optimistic bias was found. Analysis of the experience and demographic variables’ effects on risk perception and optimistic bias, and their change, provided mixed results. The lessening of risk perception and increase in optimistic bias over the period of quiescence suggest that risk communicators and emergency managers should direct attention toward reversing these trends to increase disaster preparedness.     

A Fuzzy‐Based Risk Assessment Framework for Autonomous Underwater Vehicle Under‐Ice Missions

The use of autonomous underwater vehicles (AUVs) for various scientific, commercial, and military applications has become more common with maturing technology and improved accessibility. One relatively new development lies in the use of AUVs for under‐ice marine science research in the Antarctic. The extreme environment, ice cover, and inaccessibility as compared to open‐water missions can result in a higher risk of loss. Therefore, having an effective assessment of risks before undertaking any Antarctic under‐ice missions is crucial to ensure an AUV's survival. Existing risk assessment approaches predominantly focused on the use of historical fault log data of an AUV and elicitation of experts’ opinions for probabilistic quantification. However, an AUV program in its early phases lacks historical data and any assessment of risk may be vague and ambiguous. In this article, a fuzzy‐based risk assessment framework is proposed for quantifying the risk of AUV loss under ice. The framework uses the knowledge, prior experience of available subject matter experts, and the widely used semiquantitative risk assessment matrix, albeit in a new form. A well‐developed example based on an upcoming mission by an ISE‐explorer class AUV is presented to demonstrate the application and effectiveness of the proposed framework. The example demonstrates that the proposed fuzzy‐based risk assessment framework is pragmatically useful for future under‐ice AUV deployments. Sensitivity analysis demonstrates the validity of the proposed method.     

The influence of mapped hazards on risk beliefs: a proximity-based modeling approach

Interview findings suggest perceived proximity to mapped hazards influences risk beliefs when people view environmental hazard maps. For dot maps, four attributes of mapped hazards influenced beliefs: hazard value, proximity, prevalence, and dot patterns. In order to quantify the collective influence of these attributes for viewers’ perceived or actual map locations, we present a model to estimate proximity‐based hazard or risk (PBH) and share study results that indicate how modeled PBH and map attributes influenced risk beliefs. The randomized survey study among 447 university students assessed risk beliefs for 24 dot maps that systematically varied by the four attributes. Maps depicted water test results for a fictitious hazardous substance in private residential wells and included a designated “you live here” location. Of the nine variables that assessed risk beliefs, the numerical susceptibility variable was most consistently and strongly related to map attributes and PBH. Hazard value, location in or out of a clustered dot pattern, and distance had the largest effects on susceptibility. Sometimes, hazard value interacted with other attributes, for example, distance had stronger effects on susceptibility for larger than smaller hazard values. For all combined maps, PBH explained about the same amount of variance in susceptibility as did attributes. Modeled PBH may have utility for studying the influence of proximity to mapped hazards on risk beliefs, protective behavior, and other dependent variables. Further work is needed to examine these influences for more realistic maps and representative study samples.